Bayesian Treatment of the Independent Student- t Linear Model
This article takes up methods for Bayesian inference in a linear model in which the disturbances are independent and have identical Student-t distributions. It exploits the equivalence of the Student-t distribution and an appropriate scale mixture of normals, and uses a Gibbs sampler to perform the computations. The new method is applied to some well-known macroeconomic time series. It is found that posterior odds ratios favour the independent Student-t linear model over the normal linear model, and that the posterior odds ratio in favour of difference stationarity over trend stationarity is often substantially less in the favored Student-t models. Copyright 1993 by John Wiley & Sons, Ltd.
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Volume (Year): 8 (1993)
Issue (Month): S (Suppl. Dec.)
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